Summary This paper proposesTRANSMUT‐Sparkfor automating mutation testing of big data processing code within Spark programs. Apache Spark is an engine for big data analytics/processing that hides the inherent complexity of parallel big data programming. Nonetheless, programmers must cleverly combine Spark built‐in functions within programs and guide the engine to use the right data management strategies to exploit the computational resources required by big data processing and avoid substantial production losses. Many programming details in Spark data processing code are prone to false statements that must be correctly and automatically tested. This paper explores the application of mutation testing in Spark programs, a fault‐based testing technique that relies on fault simulation to evaluate and design test sets. The paper introducesTRANSMUT‐Sparkfor testing Spark programs by automating the most laborious steps of the process and fully executing the mutation testing process. The paper describes how theTRANSMUT‐Sparkautomates the mutant generation, test execution and adequacy analysis phases of mutation testing. It also discusses the results of experiments to validate the tool and argues its scope and limitations.

Cloud computing became a reality, and many companies are now moving their data-centers to the cloud. A concept that is often linked with cloud computing is Infrastructure as a Service (IaaS): the computational infrastructure of a company can now be seen as a monthly cost instead of a number of different factors. Recently, a large number of organizations started to replace their relational databases with hybrid solutions (NoSQL DBs, Search Engines, ORDBs). These changes are motivated by (i) performance improvements on the overall performance of the applications and (ii) inability to a RDBMS to provide the same performance of a hybrid solution given a fixed monthly infrastructure cost. However, not always the companies can exactly measure beforehand the future impact on the performance on their services by making this sort of technological changes (replace RDBMS by another solution). The goal of this systematic mapping study is to investigate the use of Service-Level-Agreements (SLAs) on database-transitioning scenarios and to verify how SLAs can be used in this processes.

The adoption of Cloud Computing technologies by the organizations has profound consequences on the way software applications are developed and used. The migration to the Cloud may be accompanied by the revision of the business process, to integrate tasks over big data. In this way, existing workflow implementations may be extended with calls to operations in Hadoop or other tools. In this work, we define a framework to implement business process over heterogeneous technologies. Our framework is based on a novel workflow engine, called μBP-AM. Workflow execution in μBP-AM is performed by successively transforming the graph obtained from a workflow definition. μBP-AM has a formal semantics, which gives a precise definition of how the workflow is implemented. μBP-AM is at the core of an extensible framework capable of supporting not only Web service operations but also Hadoop operation calls (among others). The tool described here was conceived to increase reliability and to promote interoperability. We describe a prototype implementation of our framework, as well as some experimental results. Experiments using this prototype show that compositions run in μBP-AM using about the same resources as those run by using other tools.

Existing data integration techniques have to be revisited to query big data collections on the Cloud. Service Level Agreements implement the contracts between the cloud provider and the users, and between the cloud and service providers. Given SLA heterogeneity and data integration scalability problems, we propose an SLA guided data integration for querying data on multiple clouds.

The Service Oriented Computing Paradigm proposes the construction of applications by integrating pre-existent services. Since a large number of services may be available in the Cloud, the selection of services is a crucial task in the definition of a composition. The selected services should attend the requirements of the compound application, by considering both functional and non-functional requirements (including quality and preference constraints). As the number of available services increases, the automation of the selection task becomes desirable. We propose a method for the refinement of service compositions that takes the abstract specification of a composition, the definition of concrete services and user preferences. Our algorithm produces a list of refinements in preference order. Experiments show that our method can be used in practice.

The usual way to describe the semantics of MIB objects is just to give an informal English text explaining each object's behavior. Informal descriptions are vague and incomplete. They are open to misinterpretation and may lead to inconsistent implementations. In this work we propose the use of action semantics as a simple and powerful tool for the formal description of the behavior of MIB objects. Formal descriptions may be used as the basis for systematic development, verification, and automatic generation of implementations. In our approach, operations for each MIB object can be simply added to the existing ASN.1 MIB descriptions, without implying in any modification of current standards. We initially define the semantics of the core SNMP server, as well as the snmpget and snmpset applications. This description is extensible, allowing the inclusion of any MIB. As a case study, we define the semantics of the experimental SNMP routing proxy MIB.